[R] About normality tests...

[Greg Snow]

Before doing normality tests look at fortune(117) and fortune(234).  If you still feel the need to have the computer print out a p-value for a test of exact normality, then try SnowsPenultimateNormalityTest in the TeachingDemos package.  If you want a test that is more meaningful, then look at vis.test (also in the TeachingDemos package).


-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Ralf B
> Sent: Wednesday, June 23, 2010 12:05 PM
> To: r-help at r-project.org
> Subject: [R] About normality tests...
> 
> Hi all,
> 
> I have two very large samples of data (10000+ data points) and would
> like to perform normality tests on it. I know that p < .05 means that
> a data set is considered as not normal with any of the two tests. I am
> also aware that large samples tend to lead more likely to normal
> results (Andy Field, 2005).
> 
> I have a few questions to ensure that I am using them right.
> 
> 1) The Shapiro-Wilk test requires to provide mean and sd. Is is
> correct to add here the mean and sd of the data itself (since I am
> comparing to a normal distribution with the same parameters) ?
> 
> mySD <- sd(mydata$myfield)
> myMean <- mean(mydata$myfield)
> shapiro.test(rnorm(100, mean = myMean, sd = mySD))
> 
> 2) If I just want to test each distribution individually, I assume
> that I am doing a one-sample Kolmogorov-Smirnov test. Is that correct?
> 
> 3) If I simply want to know if normality exists or not, what should I
> put for the parameter 'alternative' ? Does it actually matter?
> 
> alternative = c("two.sided", "less", "greater")
> 
> Thank you,
> Ralf
> 
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